No matter your industry or profession, you can’t get better over time without categorizing what you’re learning. And this doesn’t just mean capitalizing on your successes — it also means actively researching and understanding what went wrong in your mistakes.

This approach especially applies to learnings in the demand generation field. in general is highly experimental. Companies are constantly performing A/B tests of everything from website UX to seeing how specific keywords and content work in different channels. Usually, given enough time, companies can gain insights into what works and what doesn’t when it comes to demand generation.

But just because something works at one point doesn’t mean it will work tomorrow or the day after. It’s not enough to just keep doubling down on your successes because nothing works forever. Think of Blockbuster video — Blockbuster had a lock on the rental video business for years, but the company is now defunct because tastes and preferences switched to streaming services like Netflix.

Thus, the shelf life of information and winning strategies varies. So when it comes to successful demand generation approaches, it’s crucial to know the best way to capture and keep up with learnings and make this knowledge a shareable asset across teams and the organization as a whole.

In this article, I analyze how to do this, drawing on tactics from not just demand generation and technology experts, but other industries as well. I’ve broken it down into two categories — what you should do looking outward and what you should do looking inward at your own organization.

Looking Outward

Keep Current

In my research, I found that one crucial strategy common across many fields to address this problem is to keep up with trends in the work. For those working in demand generation, that means keeping current on trends in marketing. Surveys and research like this one from HubSpot are a good example of how to gain a broader perspective on the industry. For instance, HubSpot’s research shows that only 17% of marketers are using A/B tests — indicating that if you do use this tactic, you likely have a leg up on your competitors. Articles like this also help to counteract misconceptions with actual research, pointing out such key insights as:

  • Landing pages have much better conversion rates than pop-ups.
  • Articles that are longer than 3,000 words actually do better than shorter blogs.

Research also allows companies to establish baseline metrics from which to compare the performance of their campaigns. That HubSpot report provides industry-wide averages for demand gen, such as:

  • Email campaigns achieve on average a 17% open rate and a 4% click-through rate.
  • Across consumer products, marketing, media & publishing, and non-profit industries, very few organizations average more than 5,000 MQLs per month.
  • Ad placement and audience targeting are the top ways that advertisers drive more demand.

Use Social Media

Regardless of what industry you work in, to know what works in demand generation, you have to use social media. Whether in law, education, or technology, it’s advisable to find people who you trust and are industry leaders and follow their LinkedIn and Twitter accounts especially. The amount of learning that can be derived is astonishing and this ensures you’re never falling behind on the latest insights. When life returns to normal after COVID, demand generation leaders should also consider going to see many of these leading thinkers at industry events like Recode and TechCrunch.

Looking Inward

Build Learning Ecosystems

Though it’s about the medical profession, this Harvard Business article offers relevant advice for how to get the most of your demand generation learnings internally. To truly capitalize on your understanding, just as hospitals have created committees to understand what’s working best in patient care, companies should create teams that discuss and analyze the results of . These teams shouldn’t just be made up of those working in demand generation, but also representatives from other departments in the company who can each put their unique perspectives on why changes may or may not work and the implications of putting them into place.

As part of these ecosystems, Dr. Atul Gawande’s famed New Yorker article from 2011 on coaching also offers some insights. Coaching makes everyone better, whether you’re a doctor, a teacher, a basketball player, or a demand generator. Just as the best teams break down the tape before and after games to adjust to their opponents and know what to expect, so should those in demand generation. This quote from Gawande’s article is especially pertinent:

California researchers in the early nineteen-eighties conducted a five-year study of teacher-skill development in eighty schools, and noticed something interesting. Workshops led teachers to use new skills in the classroom only ten per cent of the time. Even when a practice session with demonstrations and personal feedback was added, fewer than twenty per cent made the change. But when coaching was introduced—when a colleague watched them try the new skills in their own classroom and provided suggestions—adoption rates passed ninety per cent. A spate of small randomized trials confirmed the effect. Coached teachers were more effective, and their students did better on tests.

Thus, on these teams, it’s not enough to just discuss results. There should also be mentorship and 360 degree feedback for all members.

Incorporating Trends into the Work

Earlier, I mentioned how important it is to stay abreast of current trends in demand generation. But it’s also crucial that effective learning ecosystems involve discussions of these trends with all team members. For instance, by following the latest research such as this Chief Marketer 2020 B2B Marketing Outlook Survey, demand gen teams can know that email continues to offer the highest ROI of any form of engagement. They will also learn about the prevalence of account-based marketing, how 87% of those in one industry survey found it outperformed all other marketing investments, and how it can be applied to their business.

A/B Testing Knowledge

But to really get the most out of your A/B testing, once you have these top-down approaches nailed, you also need to understand what’s most effective with A/B testing itself. Insights from resources like these analyzes from Investpro,, and offer a lot of recommendations on how to know whether to trust your A/B testing results or not. Here are some common strategies companies should adopt to track learnings:

  • Don’t run one test, run many to ensure your results are accurate. Once you get a clear result, run the test again with a variety of variations to ensure the results hold up across many contexts. For instance, see what happens when you push 100% of traffic to a winning variation.
  • Remember that A/B testing isn’t about winners and losers, it’s about learning. And learning occurs as much from the failures as the successes.
  • Analyze your results from a number of different angles to see not just whether the test worked or did not, but why. As the Investpro article points out, look at them by:
    • Traffic source
    • Visitor type
    • Browser type
    • Device type
  • If something fails, re-examine your hypothesis to determine what assumptions you made that were wrong. This can include revalidating your research data. Even if that data is correct, your prediction may have been wrong, as a problem can have multiple causes or solutions.
  • Factor in the sample size, significance level, test duration, number of conversions, and tightly segmented results to get a better idea of why the tests turned out the way they did. 
  • Know the errors common in demand generation. As this helpful article points out, these are 12 frequent A/B test mistakes:
  1. Calling A/B test early
  2. Not running tests for full weeks
  3. Doing A/B tests without enough traffic or conversions
  4. Not basing tests on a hypothesis
  5. Not sending test data to Google Analytics
  6. Wasting time and traffic on clearly invaluable tests
  7. Failing to understand false positives
  8. Running multiple tests simultaneously on overlapping traffic
  9. Ignoring small achievements
  10. Running too few tests
  11. Being unaware of validity threats
  12. Giving up after a first test fails

Track What You Learn

Ultimately, to get the most of your learning, the learning ecosystems and teams that companies create to focus on demand gen also have to implement metrics and tracking systems to formalize the dissemination of information. Frequent meetings with diverse teams in which software or spreadsheets are used to track results are a solid approach to solving this problem. But as I pointed out at the beginning, companies have to constantly return to their strategies that seem most effective to ensure that those tactics haven’t become stale.